graph TD A[AI Analyst Inputs] --> B[Firm Data] A --> C[Industry Data] A --> D[Macro Data] A --> E[Filings] A --> F[Sentiment] A --> G[Innovation]
2025-05-02
After losing, Kasparov didn’t give up — he adapted.
He introduced “centaur chess”, pairing human intuition with machine power.
Kasparov’s idea of man + machine wasn’t just for chess.
This study brings that question into finance:
Can human analysts and AI models compete — or collaborate — in forecasting the market?
To explore this, the authors created an AI analyst and compared it to real professionals.
In this study, both human and AI analysts forecast 12-month-ahead stock returns using target prices.
This study addresses three core questions:
Can an AI analyst predict 12-month-ahead stock returns more accurately than human analysts?
When do human analysts outperform AI — and why?
Does combining AI and human forecasts improve accuracy and reduce large errors?
The AI analyst uses an ensemble of machine learning models:
Long Short-Term Memory (LSTM) networks — capture sequential patterns in financial time series
Random forest — robust to noise and effective with many predictors
Gradient boosting — excels at handling non-linear relationships and variable interactions
The ensemble leverages the strengths of each model type to improve predictive accuracy.
The AI analyst is trained on a wide variety of publicly available information.
The model draws from six key categories of inputs:
graph TD A[AI Analyst Inputs] --> B[Firm Data] A --> C[Industry Data] A --> D[Macro Data] A --> E[Filings] A --> F[Sentiment] A --> G[Innovation]
Structure:
Structured Inputs
Textual and Alternative Inputs
Firm Data: - Size, book-to-market, ROA - Leverage, accruals - Past returns (1–36 months), volatility - Amihud illiquidity
Industry Data: - Fama-French industry groupings - Industry competition (text-based) - Product market fluidity
Macro Data: - GDP growth, CPI, oil prices - Treasury yields, credit spreads - Market-level illiquidity
Filings (10-K, 10-Q, 8-K): - Sentiment and tone (Loughran–McDonald) - Readability and complexity - Text similarity and novelty
Sentiment: - RavenPack news sentiment - Twitter-based firm sentiment (Cao et al. 2021a)
Innovation: - Patent value estimates (Kogan et al. 2017)
AI uses only public data, with no access to analyst forecasts or private information.
While the AI model performs better overall, human analysts outperform in specific contexts:
Combining AI forecasts with human analyst inputs creates a hybrid “centaur” analyst.
Extreme errors = forecasts in the top 10% of squared prediction errors
Man + Machine avoids: